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A two-level classification-based color constancy

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Abstract

This paper addresses color constancy, the problem of finding true color of objects independent of the light illuminating the scene. However, many algorithms exist in this scope, they are all based on specific assumptions and none of them is universal. Therefore, in order to achieve better performance, some new color constancy methods have been proposed, which most of them are combinational algorithms. In this paper, a new combinational method is proposed; the proposed method consists of two steps: first, a classifier is used to determine the best group of color constancy algorithms for the input image; then, some of the algorithms in this group are combined to estimate the scene illuminant. In this way, it always combines the algorithms that have good performance for the input image, and as a result, the overall performance increases. The proposed method has been evaluated using multiple benchmark datasets, and the experimental results showed that the proposed approach outperformed current state-of-the-art algorithms.

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References

  1. Muselet, D., Funt, B.: Color invariants for object recognition. In: Fernandez-Maloigne, C. (ed.) Advanced Color Image Processing and Analysis, pp. 327–376. Springer, New York (2013)

    Chapter  Google Scholar 

  2. Foster, D.H.: Color constancy. Vis. Res. 51, 674–700 (2011)

    Article  Google Scholar 

  3. Faghih, M.M., Moghaddam, M.: Neural gray edge: improving gray edge algorithm using neural network. In: IEEE International Conference on Image Processing (ICIP). Brussels, Belgium (2011)

  4. Akhavan, T., Moghaddam, M.: A new combining learning method for color constancy. In: International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 421–425 (2010)

  5. Akhavan, T., Moghaddam, M.: A color constancy method using fuzzy measures and integrals. Opt. Rev. 18, 273–283 (2011)

    Article  Google Scholar 

  6. Agarwal, V., Gribok, A.V., Abidi, M.A.: Machine learning approach to color constancy. Neural Netw. 20, 559–563 (2007)

    Article  MATH  Google Scholar 

  7. Cardei, V., Funt, B.V., Barnard, K.: Estimating the scene illumination chromaticity using a neural network. J. Opt. Soc. Am. 19, 2374–2386 (2002)

    Article  Google Scholar 

  8. Stanikunas, R., Vaitkevicius, H., Kulikowski, J.J.: Investigation of color constancy with a neural network. Neural Netw. 17, 327–337 (2004)

    Article  MATH  Google Scholar 

  9. Gijsenij, A., Gevers, T., van deWeijer, J.: Generalized gamut mapping using image derivative structures for color constancy. Int. J. Comput. Vis. 86, 127–139 (2010)

    Article  Google Scholar 

  10. Finlayson, G.D., Hordley, S.D.: Gamut constrained illuminant estimation. Int. J. Comput. Vis. 67, 93–109 (2006)

    Article  Google Scholar 

  11. Ebner, M.: Evolving color constancy. Pattern Recognit. Lett. 27, 1220–1229 (2006)

    Article  Google Scholar 

  12. Finlayson, G.D., Hordley, S.D., Hubel, P.M.: Color by correlation: a simple, unifying framework for color constancy. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001)

    Google Scholar 

  13. Buchsbaum, G.: A spatial processor model for object colour perception. J. Frankl. Inst. 310, 1–26 (1980)

    Article  Google Scholar 

  14. Provenzi, E., Gatta, C., Fierro, M., Rizzi, A.: A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008)

    Google Scholar 

  15. Land, E.: The retinex theory of color vision. Sci. Am. 237, 108–128 (1977)

    Article  Google Scholar 

  16. Finlayson, G.D., Trezzi, E.: Shades of gray and colour constancy. In: Presented at the Color Imaging Conference. Scottsdale, Arizona (2004)

  17. van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Trans. Image Process. 16, 2207–2214 (2007)

    Article  MathSciNet  Google Scholar 

  18. Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. IEEE Trans. Pattern Anal. Mach. Intell. 99, 687–698 (2010)

    Google Scholar 

  19. Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Automatic color constancy algorithm selection and combination. Pattern Recognit. 43, 695–705 (2010)

    Article  MATH  Google Scholar 

  20. Ebner, M.: Color Constancy: Wiley-IS &T Series in Imaging, Science and Technology (2007)

  21. Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13, 891–906 (1991)

    Article  Google Scholar 

  22. Gijsenij, A., Gevers, T., van de Weijer, J.: Improving color constancy by photometric edge weighting. Pattern Anal. Mach. Intell. IEEE Trans. 34, 918–929 (2012)

    Article  Google Scholar 

  23. Barnard, K., Cardei, V., Funt, B.: A comparison of computational color constancy algorithms; part one: methodology and experiments with synthesized data. IEEE Trans. Image Process. 11, 972–984 (2002)

    Google Scholar 

  24. Geusebroek, J.-M., Smeulders, A.: A six-stimulus theory for stochastic texture. Int. J. Comput. Vis. 62, 7–16 (2005)

    Article  Google Scholar 

  25. Barnard, K., Finlayson, G., Funt, B.: Colour constancy for scenes with varying illumination. In: Buxton, B., Cipolla, R. (eds.) Computer Vision—ECCV ’96, vol. 1065, pp. 1–15. Springer, Berlin (1996)

    Google Scholar 

  26. Ciurea, F., Funt, B.: A large image database for color constancy research. In: Proceedings of the 11th Color Imaging Conference, pp. 160–164 (2003)

  27. Dongjin, S., Dacheng, T.: Biologically inspired feature manifold for scene classification. Image Process. IEEE Trans. 19, 174–184 (2010)

    Article  Google Scholar 

  28. Torralba, A., Oliva, A.: Statistics of natural image categories. Network (Bristol, England) 14, 391–412 (2003)

  29. Bayazit, U.: Adaptive spectral transform for wavelet-based color image compression. IEEE Trans. Circuits Syst. Video Technol. 21, 983–992 (2011)

    Article  Google Scholar 

  30. Idris, F., Panchanathan, S.: Storage and retrieval of compressed images using wavelet vector quantization. J. Vis. Lang. Comput. 8, 289–301 (1997)

    Google Scholar 

  31. Yongsheng, D., Jinwen, M.: Wavelet-based image texture classification using local energy histograms. Signal Process. Lett. IEEE 18, 247–250 (2011)

    Article  Google Scholar 

  32. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29, 411–426 (2007)

    Article  Google Scholar 

  33. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42, 145–175 (2001)

    Article  MATH  Google Scholar 

  34. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  35. Abe, S.: Support Vector Machines for Pattern Classification. Springer, New York (2010)

    Book  MATH  Google Scholar 

  36. Wilamowski, B.M., Hao, Y.: Improved computation for Levenberg–Marquardt training. IEEE Trans. Neural Netw. 21, 930–937 (2010)

    Article  Google Scholar 

  37. Gijsenij, A., Gevers, T., van de Weijer, J.: Computational color constancy: survey and experiments. IEEE Trans. Image Process. 20, 2475–2489 (2011)

    Article  MathSciNet  Google Scholar 

  38. Color Constancy website. Available: http://www.colorconstancy.com

  39. Gehler, P.V., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)

  40. Shi, L., Funt, B.: Re-processed Version of the Gehler Color Constancy Dataset of 568 Images. Available: http://www.cs.sfu.ca/~colour/data/

  41. Barnard, K., Martin, L., Funt, B., Coath, A.: A data set for color research. Color Res. Appl. 27, 147–151 (2002)

    Article  Google Scholar 

  42. Hogg, R.V., Tanis, E.A.: Probability and Statistical Inference. Prentice Hall, Englewood Cliffs, NJ (2001)

    Google Scholar 

  43. Anthony, M., Bartlett, P.L.: Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge, MA (1999)

    Book  MATH  Google Scholar 

  44. Levenberg, K.: A method for the solution of certain nonlinear problems in least squares. Q. Appl. Math. 2, 164–168 (1994)

    MathSciNet  Google Scholar 

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Acknowledgments

We would like to thank from Iran National Science Foundation for their financial support of this research.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Shahid Beheshti University, 1983963113 , G. C., Tehran, Iran

    Mohammad Mehdi Faghih & Mohsen Ebrahimi Moghaddam

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  1. Mohammad Mehdi Faghih
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  2. Mohsen Ebrahimi Moghaddam
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Correspondence to Mohsen Ebrahimi Moghaddam.

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Faghih, M.M., Moghaddam, M.E. A two-level classification-based color constancy. SIViP 9, 1299–1316 (2015). https://doi.org/10.1007/s11760-013-0574-7

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  • DOI: https://doi.org/10.1007/s11760-013-0574-7

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